Method and apparatus for determining a fall risk

ABSTRACT

According to an aspect, there is provided a computer-implemented method of determining a fall risk of a subject, the method comprising receiving a first data set indicative of movement of the subject; receiving a second data set indicative of context information of the subject; selecting a part of the first data set based on the second data set; and determining a fall risk based on the selected part of the first data set.

FIELD OF THE INVENTION

The disclosure relates to a method and apparatus for determining therisk of a subject falling.

BACKGROUND TO THE INVENTION

Falls are a significant problem, particularly for elderly people. About30 percent of people over 65 years old fall at least once a year.Home-based fall-prevention exercise programs that include balancetraining, muscle strengthening and a walking plan have been found to beeffective in reducing the occurrence of falls by 30-46%. Fall-riskassessment is important to identify elderly people at risk of falling,to tailor exercises for optimizing fall prevention intervention programsand to monitor progression of fall risk.

In some cases a subject can fill in a questionnaire to subjectivelyassess their fall risk. Based on the answers the subject can getfeedback in order to teach the subject how not to fall (e.g. to reducetheir risk of falling).

Caregivers can provide a much better estimation of fall risk throughobjective assessment of physical performance (e.g. walking quality,strength, balance and reaction time). The association between physicalperformance test outcomes and fall risk has been well established.

However, for objective fall risk assessment people have to presentthemselves at a clinic where dedicated hardware and clinicians areneeded. These tests are often obtrusive and require specific movementsor activities to be performed. This results in a lowmonitoring/observation rate (e.g. perhaps only once a year). Moreover,subjects often present themselves for the first time to the clinic onlyafter a fall occurred, i.e. when it is already too late.

It is therefore desirable to be able to monitor fall risk in the homeenvironment. Systems for predicting fall risk based on measurements fromone or more sensors that can be used in the home environment are known,for example in U.S. Pat. No. 7,612,681. However, this monitoring canrequire obtrusive and expensive dedicated hardware, for example a camerasystem or a network of other types of sensors that needs to be installedin the home.

Therefore there is a need for an improved method and apparatus fordetermining a fall risk.

SUMMARY OF THE INVENTION

The information gathered by one or more sensors in free livingconditions (e.g. in the home environment) largely depends on theenvironmental challenges and the movement intention of the subject,which is not captured by the algorithm that evaluates the sensormeasurements and determines the fall risk. It is therefore difficult tocompare walking or other movements over time to measure changes in fallrisk (e.g. in terms of ambulatory ability progression) since the contextof the movements is different.

Thus, according to a first aspect, there is provided acomputer-implemented method of determining a fall risk of a subject, themethod comprising receiving a first data set indicative of movement ofthe subject; receiving a second data set indicative of contextinformation of the subject; selecting a part of the first data set basedon the second data set; and determining a fall risk based on theselected part of the first data set.

According to a second aspect, there is provided a computer programproduct comprising a computer readable medium having computer readablecode embodied therein, the computer readable code being configured suchthat, on execution by a suitable computer or processor, the computer orprocessor is caused to perform the method of the first aspect.

According to a third aspect, there is provided an apparatus fordetermining a fall risk of a subject, the apparatus comprising aprocessing unit configured to receive a first data set indicative ofmovement of the subject; receive a second data set indicative of contextinformation of the subject; select a part of the first data set based onthe second data set; and determine a fall risk based on the selectedpart of the first data set.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the invention will now be described, by way ofexample only, with reference to the following drawings, in which:

FIG. 1 is a block diagram of an apparatus according to an embodiment;

FIG. 2 is a flow chart illustrating a general method of determining afall risk;

FIG. 3 illustrates a first data set indicative of movement of thesubject;

FIG. 4 illustrates a second data set indicative of context informationof the subject;

FIG. 5 illustrates a first data set and a second data and the selectionof parts of the first data set; and

FIG. 6 illustrates a first data set and a second data set obtained fromtwo different sensors and the selection of parts of the first data set.

DETAILED DESCRIPTION OF EMBODIMENTS

As described above it is difficult to compare walking or other movementsof a subject over time to measure changes in fall risk (e.g. in terms ofambulatory ability progression) since the context of the movements canbe different at different times. For example a subject may walk unaidedduring one time interval, and walk using a walking aid (such as awalking stick or walking frame) during another time interval, and thusit may not be appropriate to directly compare the subject's walkingability during each time interval (e.g. in assessing progression of fallrisk, since the context of the walking is different).

Thus, embodiments provide that context information relating to movementsof a subject is received or measured, and this context information isused to select a part of the measurements of the movements. A fall riskcan then be determined from the selected part of the movementmeasurements. In this way, a large part of the variability betweengeneral types of movements (e.g. walking, jogging, standing still,exercising, sitting down, standing up, etc.) can be accounted for due todifferent contexts to the movements (e.g. using/not using a walking aid,moving in a well-lit/poorly-lit area, etc.), and thus the measure offall risk determined from the movement measurements can be moreaccurate, as well as providing a more accurate indication of theprogression or changes in fall risk over time.

An embodiment of an apparatus 2 for determining a fall risk of a subjectis shown in FIG. 1. The apparatus 2 comprises one or more movementsensors 4 that measure the movements or other motion of the subject. Insome embodiments the movement sensor 4 is an accelerometer that measuresaccelerations in three dimensions, in which case the movement sensor 4can be worn or carried by the subject. The movement sensor 4 cancomprise an altitude sensor (e.g. an air pressure sensor) that measuresthe altitude of the subject, or changes in the altitude or height of thesubject (for example to determine if the subject has gone or is going upor down the stairs). The movement sensor 4 can comprise a positionsensor for measuring the position of the subject. The position sensorcan be, for example, a satellite positioning system sensor, such as aGPS (Global Positioning System) receiver, that measures the location ofthe subject (and in some cases the speed of movement of the subject aswell). The movement sensor 4 can comprise one or more cameras or otherimaging devices that record images of the subject or the subject'ssurroundings, in which case the movement sensor 4 can be located in theenvironment of the subject. In some embodiments, the apparatus 2 cancomprise multiple movement sensors 4 (e.g. two or more of (and/ormultiple instances of) an accelerometer, air pressure sensor, a positionsensor and an imaging device). Those skilled in the art will be aware ofother types of movement sensor that can be used in an apparatus 2.

In the case of an accelerometer, the accelerometer can measure themagnitude of acceleration along three orthogonal axes (e.g. labelled X,Y and Z) and output three signals, each representing the magnitude ofacceleration along a respective one of the axes, or output a singlesignal that is a composite of the accelerations measured along the threeorthogonal axes. The accelerometer 4 (or more generally the movementsensor 4) can operate with any suitable sampling frequency, for example50 Hz, i.e. the accelerometer 4 can output an acceleration measurementevery 1/50th of a second, or for example 10 Hz. The output of themovement sensor(s) 4 is referred to generally as a “first data set”herein, and represents measurements of movements of the subject overtime from each of the movement sensor(s) 4 (e.g. accelerationmeasurements from an accelerometer and air pressure measurements from anair pressure sensor).

The measurements of movements (first data set) are provided to aprocessing unit 6 in the apparatus 2. The processing unit 6 processesthe measurements to determine a fall risk of the subject, as describedin more detail below. The processing unit 6 also controls the operationof the apparatus 2, for example controlling the initiation of thecollection of measurements by the movement sensor 4, and/or otherfunctions and operations of the apparatus 2. The processing unit 6 canbe implemented in numerous ways, with software and/or hardware, toperform the various functions required. The processing unit 6 maycomprise one or more microprocessors that may be programmed usingsoftware to perform the required functions. The processing unit 6 may beimplemented as a combination of dedicated hardware to perform somefunctions and a processor (e.g., one or more programmed microprocessorsand associated circuitry) to perform other functions. Examples ofprocessing components that may be employed in various embodiments of thepresent disclosure include, but are not limited to, conventionalmicroprocessors, application specific integrated circuits (ASICs), andfield-programmable gate arrays (FPGAs). In some embodiments, componentsand functionality of the processing unit 6 can be distributed acrossmultiple locations in multiple units or modules.

In various implementations, the processing unit 6 may be associated withone or more storage media, shown as memory unit 8 in FIG. 1. The memoryunit 8 can be part of the processing unit 6, or it can be a separatecomponent in the apparatus 2 that is connected to the processing unit 6.The memory unit 8 can comprise any suitable or desired type of volatileor non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM.The memory unit 8 can be used for storing computer program code that canbe executed by the processing unit 6 to perform the method describedherein. The memory unit 8 can also be used to store signals ormeasurements from the movement sensor 4 and/or other sensors in theapparatus 2, and/or information relating to the fall risk determined bythe processing unit 6.

As noted above, context information is used to identify a part of themovement measurements that are to be used to determine a fall risk.Thus, the apparatus 2 comprises one or more sensors that providescontext information for the subject. In some embodiments, contextinformation can be derived from the movement measurements from themovement sensor 4. In other embodiments, context information is also oralternatively provided by one or more context sensors 10. Generally, thecontext information is any type of information or measurement that canbe used to determine the context of movements by the subject.

Various examples of context sensor(s) 10, and the context informationthat can be measured by the context sensor(s) 10, are provided below. Insome cases the context sensor(s) 10 can monitor the environment aroundthe subject (e.g. the lighting conditions, the ambient noise or sounds,or the location of the subject). In some cases, context sensor(s) 10 canbe associated with an object or device that the subject may use, and thecontext sensor 10 can provide an indication of whether the object ordevice is being used by the subject. As such, a context sensor canitself be a ‘movement sensor’ (e.g. an accelerometer), but it isassociated with a particular object or device rather than the subject,and thus measures the movements of the object or device. Suitableobjects or devices include walking aids, medication dispensers, beds,etc. In some implementations, multiple context sensors 10 (of the sameor different types) are located throughout the home environment of thesubject so that many different activities and contexts of the subjectcan be measured. In these cases, the context sensors 10 may be part ofan existing ‘smart home’ arrangement of devices and sensors.

An example of context information derived from the movement measurementsfrom the movement sensor 4 is where an air pressure sensor measures acertain height change per step which can be interpreted as the subjectclimbing stairs with a certain steepness. Another example is whereposition measurements from a satellite positioning system indicate thatthe subject is outside on a track, a path (e.g. hill path) or a beach.

The measurements by the context sensor(s) 10 are provided to theprocessing unit 6 for use in determining the part of the first data set(movement measurements) that can be used to determine the fall risk. Thecontext information (or more generally the measurement signals from theone or more context sensor(s) 10) is referred to herein as a “seconddata set”, and represents information on the context over time.

In some embodiments, all of the components of the apparatus 2 are partof the same device, e.g. the movement sensor 4, context sensor(s) 10 andprocessing unit 6 are in a single housing. In these embodiments, theapparatus 2 can be portable or wearable so that it can be carried orworn by the subject. In some embodiments, the apparatus 2 is implementedin a fall detection system, in which case the processing unit 6 can beconfigured to both determine a fall risk from the first data set andsecond data set, and determine whether the subject has suffered a fallfrom at least the first data set.

However, in other embodiments the movement sensor 4 and/or the contextsensor(s) 10 are in a separate device or housing (or respective devicesand housings) to the processing unit 6. Where the movement sensor 4and/or context sensor(s) 10 are provided in a separate device(s) orhousing(s) to the processing unit 6, appropriate circuitry or componentscan be provided to enable the movement measurements (first data set)and/or context information (second data set) to be sent (e.g.transmitted) to the processing unit 6. In some examples the movementsensor(s) 4 can be configured to be worn or carried by the subject (forexample worn on their arm, leg, chest, waist, torso, or worn as apendant around their neck). In these cases the processing unit 6 can bepart of a personal electronic device such as a smartphone, tabletcomputer, laptop computer or desktop computer, or part of anotherelectronic device, such as a base unit or hub unit for the movementsensor 4, or part of a remote server (e.g. located in the cloud, i.e.accessible via the Internet), in which case the measurements from themovement sensor 4 can be sent wirelessly to the processing unit 6 in theelectronic device using any suitable communication protocol (e.g. Wi-Fi,Bluetooth, or a cellular telecommunication protocol) so that the fallrisk can be determined.

In some embodiments the processing unit 6 can receive the measurementsfrom the movement sensor 4 and context sensor(s) 10 in real-time or nearreal-time (e.g. with the only delay being due to the signal processingrequired to transmit or convey the measurements to the processing unit6. In other embodiments (including embodiments where the movement sensor4 and/or context sensor(s) 10 are separate from the processing unit 6,the measurements from the movement sensor 4 and/or context sensor(s) 10can be stored in memory unit 8 and the processing unit 6 can retrieveand analyse the previously-obtained measurements from the memory unit 8when a fall risk is to be determined.

As noted above, in some embodiments the processing unit 6 may be part ofa smartphone or other general purpose computing device that can beconnected to or otherwise receive a measurement signal from movementsensor 4 and context sensor(s) 10, but in other embodiments theapparatus 2 can be an apparatus that is dedicated to the purpose ofdetermining a fall risk for a subject. In embodiments where theprocessing unit 6 is part of a smartphone or other general purposecomputing device, the movement sensor 4 could be the accelerometerand/or other type of movement sensor typically found in such asmartphone (e.g. a gyroscope).

It will be appreciated that FIG. 1 only shows the components required toillustrate various embodiments of the apparatus 2, and in a practicalimplementation the apparatus 2 will comprise additional components tothose shown. For example, the apparatus 2 may comprise a battery orother power supply for powering the apparatus 2, a communication modulefor enabling the information on a determined fall risk to becommunicated to another device, e.g. a base unit for the apparatus 2 ora remote computer, a location or position sensor for determining thelocation or position of the apparatus 2 (and thus the subject), e.g. aGlobal Positioning System (GPS) receiver, and/or one or more userinterface components that allow the subject or another user to interactand control the apparatus 2. As an example, the one or more userinterface components could comprise a switch, a button or other controlmeans for activating and deactivating the apparatus 2 and/or fall riskdetermination process. The user interface components can also oralternatively comprise a display or other visual indicator for providinginformation to the subject and/or other user about the operation of theapparatus 2, including displaying information on a determined fall risk,and/or information to educate the subject about exercises, activities ortasks to perform or avoid in order to reduce their fall risk.

The flow chart in FIG. 2 illustrates a general method of determining afall risk according to an embodiment. The method can be performed byprocessing unit 6.

In step 101, a first data set that is indicative of movement of thesubject is received. The first data set contains measurements ofmovements of the subject over a period of time. The first data set isobtained by one or more movement sensors 4 that measure the movements ofthe subject. As noted above, the movement measurements can includemeasurements from multiple sensors, for example, accelerationmeasurements, position measurements, height/altitude measurements,camera images, etc. The first data set can be received directly from themovement sensor(s) 4 (e.g. in the case where the processing unit 6 andmovement sensor(s) 4 are in the same or different devices), or the firstdata set can be retrieved from a memory unit 8. The former case isuseful where the fall risk is to be determined in real-time or nearreal-time. The first data set may comprise the raw movementmeasurements, e.g. acceleration samples in the case of an accelerometer4, or movement measurements that have been processed or filtered, e.g.to remove noise and/or to identify the specific type of movements thatthe measurements relate to (e.g. walking, sitting, standing still,performing a sit to stand movement, etc.). In the latter case, thoseskilled in the art will be aware of various algorithms and techniquesthat can be used to identify those specific types of movements, and thusfurther details are not provided herein.

FIG. 3 shows an example of a first data set that comprises accelerationmeasurements 50 and altitude measurements 52 obtained over a short timewindow of 130 seconds. It will of course be appreciated that in practicethe first data set will cover a much larger time period, for examplehours, days or weeks. The raw acceleration measurements are shown, butthe altitude measurements 52 have been obtained from air pressuremeasurements. The results of some processing of the accelerationmeasurements are shown in FIG. 3, with the circles 54 on various peaksin the acceleration measurements indicating where a step by the subjecthas been identified. A step may be identified, for example, where themagnitude of the acceleration exceeds a threshold (e.g. indicated bydashed line), although those skilled in the art will be aware of otheralgorithms or rules that can be used to identify steps. Where steps havebeen identified in the acceleration measurements, the steps and/orassociated accelerations/movements have been further analysed and fourspecific types of movements have been identified as shown by the boxesin FIG. 3. The first is shown by box 58 and represents the subjectwalking where there are three or more steps in a row that can be usedfor counting the subject's steps. The second is shown by box 60 whichcorresponds to the subject walking up and down stairs (which can also beseen in the altitude measurements). The third is shown by box 62 andrepresents the subject walking for some distance, and the measurementsin this box 62 can be used to evaluate the subject's walking, e.g. forstride regularity, stride length, etc. The fourth is shown by box 64 andrepresents the subject getting up from a chair.

In step 103, a second data set that is indicative of context informationof the subject is received. Context information can be any type ofinformation or measurement that can be used to determine the context ofparticular movements by the subject. The second data set, which cancomprise measurements from one or more sensors 4/10, can be receiveddirectly from a sensor (e.g. the movement sensor 4 and/or one or morecontext sensors 10), or the second data set can be retrieved from amemory unit 8. The second data set may comprise the raw sensormeasurements, e.g. acceleration samples in the case of an accelerometer4 or context measurement samples in the case of a context sensor 10, ormeasurements that have been processed or filtered, e.g. to remove noiseand/or to identify the context over time. It will be appreciated thatsince the second data set is used to identify the context of themovements represented in the first data set, the first data set and thesecond data set should contain measurements that at least cover the sametime period. Thus, in the embodiments where the first data set and thesecond data set are received directly from the movement sensor 4 (andoptionally one or more context sensors 10), it will be appreciated thatsteps 101 and 103 are performed at the same time. In embodiments wherethe first data set and the second data set are received from a memoryunit 8, it will be appreciated that steps 101 and 103 can be performedat the same time, or at different times.

FIG. 4 shows an example of a second data set that comprises measurementsfrom a number of sensors 10 that detect the presence of the subject in aparticular room over a period of one day (24 hours). It will of coursebe appreciated that in practice the second data set will cover a shorteror longer time period. The sensors 10 can be, for example, passiveinfrared, PIR, sensors position in each room. In this example the seconddata set comprises presence measurements for the subject in sixdifferent rooms, namely the toilet, bathroom, bedroom, kitchen, livingroom and hallway with the sensor providing an ‘on’ signal when presenceor movement is detected, and an ‘off’ signal when presence or movementis not detected. Typically an ‘off’ signal occurs shortly after (e.g. afew seconds or minutes after) an ‘on’ signal. A next ‘on’ signal mayfollow quickly if the subject is still present in that area/room. InFIG. 4 a series of closely following ‘on’ and ‘off’ signals are groupedinto one line representing presence in the area/room.

Next, in step 105, a part of the first data set is selected based on thesecond data set. As noted above, there can be significant variability inthe way that a subject performs or carries out a particular movement(e.g. walking, jogging, exercising, etc.), and this variability can beaccounted for due to the context being different for different instancesof a particular movement (e.g. walking using a walking aid and walkingunaided), and thus the context information in the second data set isused to select a part of the first data set that is to be used todetermine the fall risk of the subject.

Where the first data set and/or the second data set received in steps101/103 respectively comprise raw sensor measurements, step 105 cancomprise processing the movement measurements to identify the specifictype of movements that the measurements relate to (e.g. walking,sitting, standing still, etc.) and/or processing the measurements toidentify the context over time (although it will also be appreciatedthat some or all of this processing can be performed in steps 101/103instead). As noted above, those skilled in the art will be aware ofvarious algorithms and techniques that can be used to identify specifictypes of movements from movement measurements, and thus further detailsare not provided herein.

The part of the first data set selected in step 105 preferably relatesto the same or similar context (as indicated by the second data set).Thus, for example, the part of the first data set selected in step 105can be the movements where the lighting conditions are the same, wherethe movements are within the subject's home environment, where thesubject is using a walking aid, etc.

As well as the same or similar context, the part of the first data setselected in step 105 preferably relates to the same or similar type ofmovement (as indicated by the first data set). Thus, for example, thepart of the first data set selected in step 105 can be a part determinedto be walking movements by the subject where the context is the same orsimilar (e.g. same or similar lighting conditions).

It will be appreciated that the selected part of the first data set doesnot have to only comprise a single contiguous portion of the first dataset, but instead the selected part can comprise several separatenon-contiguous portions of the first data set. For example the selectedpart can comprise any portion of the movement measurements that relateto walking in the same lighting conditions or walking with/without awalking aid. It will be appreciated that the portions of the movementmeasurements in the selected part of the first data set may covermovements that took place at different periods during a day, or duringdifferent days.

It will be appreciated that in addition to the above, the selected partof the first data set preferably relates to a type of movement or typesof movements that are useful for determining a fall risk.

FIG. 5 shows a first data set and a second data and the selection ofparts of the first data set. In particular, FIG. 5 shows the second dataset from FIG. 4 alongside a first data set that comprises accelerationmeasurements covering the same time period as the second data set. FIG.5 shows three examples of a ‘selected part’ of the first data set thatrelate to the same or similar context and that can be used fordetermining a fall risk of the subject. In particular, the portions ofthe first data set labelled with box 70 relate to movement data wherethe subject is walking, as shown by the acceleration measurements, inthe hallway, as shown by the context information (the presenceinformation). Thus, the selected part of the first data set couldcorrespond to portions 70, and these walking movements could be used todetermine the fall risk. Alternatively, the portions of the first dataset labelled with box 72 relate to movement data where the subject isgetting up from a sitting position (i.e. a sit to stand movement), asshown by the acceleration measurements, in the lounge, as shown by thepresence information, and therefore it can be implied that the subjectis getting up from the same chair or same type of chair each time. Thusthe selected part of the first data set could correspond to portions 72.In another alternative, the portions of the first data set labelled withbox 74 relate to movement data where the subject is getting up from asitting position (i.e. a sit to stand movement), as shown by theacceleration measurements, in the toilet, as shown by the presenceinformation, and therefore it can be implied that the subject is gettingup from the toilet each time. Thus, the selected part of the first dataset could correspond to portions 74.

FIG. 6 shows another example of a first data set and a second data setand the selection of parts of the first data set. In particular, FIG. 6shows a first data set that comprises acceleration measurements (that isthe same as the first data set in FIG. 5) and a second data set thatcomprises presence information (which is the same as in FIGS. 4 and 5)and proximity information that indicates whether the subject isproximate to (e.g. in contact with) a walking aid, a first chair or asecond chair. The second data set covers the same time period as thefirst data set. FIG. 6 shows five examples of a ‘selected part’ of thefirst data set that relate to the same or similar context and that canbe used for determining a fall risk of the subject. These five examplescorrespond to the three examples shown in FIG. 5, with the chair risesbeing further delimited to the first chair or the second chair based onthe proximity information, and the walking in the hallway furtherdelimited based on whether the subject is using a walking aid or not (asindicated by the proximity information). In particular, the portions ofthe first data set labelled with box 80 relate to movement data wherethe subject is walking, as shown by the acceleration measurements, inthe hallway, as shown by the context information (the presenceinformation), with a walking aid, as shown by the proximity information.Thus, the selected part of the first data set could correspond toportions 80, and these walking movements (with a walking aid) could beused to determine the fall risk. Alternatively, the portions of thefirst data set labelled with box 82 relate to movement data where thesubject is getting up from a sitting position (i.e. a sit to standmovement), as shown by the acceleration measurements, in the lounge, asshown by the presence information, and from the first chair (as shown bythe proximity information) and therefore it is known that the subject isgetting up from the same chair each time. Thus the selected part of thefirst data set could correspond to portions 82. In another alternative,the portions of the first data set labelled with box 84 relate tomovement data where the subject is getting up from a sitting position(i.e. a sit to stand movement), as shown by the accelerationmeasurements, in the toilet, as shown by the presence information, andtherefore it can be implied that the subject is getting up from thetoilet each time. Thus, the selected part of the first data set couldcorrespond to portions 84. In yet another alternative, the portions ofthe first data set labelled with box 86 relate to movement data wherethe subject is getting up from a sitting position (i.e. a sit to standmovement), as shown by the acceleration measurements, in the lounge, asshown by the presence information, and from the second chair (as shownby the proximity information) and therefore it is known that the subjectis getting up from the same chair (but different to portions 82) eachtime. As another alternative, the portions of the first data setlabelled with 88 relate to movement data where the subject is walking,as shown by the acceleration measurements, in the hallway, as shown bythe context information (the presence information), without using awalking aid, as shown by the proximity information. Thus, the selectedpart of the first data set could correspond to portions 88, and thesewalking movements (without a walking aid) could be used to determine thefall risk.

Once a part of the first data set has been selected, the fall risk ofthe subject is determined using the selected part of the first data set(step 107). Step 107 can be performed using conventional algorithms thatdetermine fall risk from movement measurements. The particular algorithmthat is used can depend on the type of movements covered by the selectedpart of the first data set. For example, where the selected part relatesto walking (e.g. as covered by portions 70 in FIG. 5 or portions 80 inFIG. 6), the algorithm can be one that evaluates characteristics ofwalking to determine a fall risk. Exemplary techniques are described inWO 2010/026513 and WO 2011/04322. Where the selected part relates to sitto stand transfers (e.g. as covered by portions 72 or 74 in FIG. 5 orportions 82, 84 or 86 in FIG. 6), the algorithm can be one thatevaluates characteristics of that type of movement to determine a fallrisk. Exemplary techniques are described in WO 2010/035187, WO2013/001411 and WO 2014/083538.

In some embodiments, the fall risk assessment can be based on changes inthe performance of certain movements (e.g. walking, standing still,getting up from a chair, etc.) over time.

In some embodiments, the fall risk is determined only from the selectedpart of the first data set (i.e. the second data set is not used todetermine the fall risk itself), but in other embodiments the fall riskis determined from the selected part of the first data set and thecontext information corresponding (in time) to the selected part of thefirst data set.

In some embodiments, the output of step 107 is a score that representsthe subject's fall risk (e.g. a numerical score), a general indicator offall risk (e.g. high, medium or low), and/or an indicator of a change infall risk over time (e.g. higher risk/lower risk).

Thus, the method in FIG. 2 provides that context information (the seconddata set) relating to movements of a subject is received or measured,and this context information is used to select a part or parts ofmeasurements of the movements (the first data set). A fall risk is thendetermined from the selected part of the movement measurements. In thisway, it is possible to improve the reliability and sensitivity of thefall risk assessment since variability in the subject's movements (andthus variability in the determined fall risk) that is due to changes inthe context of the movements can be excluded (or substantiallyexcluded). It will be appreciated from the examples below that the useof the context information to select part of the first data set improvesa fall risk derived from a relative assessment of different instances ofthe same type of movement (since those instances in the first data setwill occur in the same context), and/or improve the absolute assessmentof fall risk since the context of a specific movement can be taken intoaccount by the fall risk assessment algorithm.

It will be appreciated that the method of FIG. 2 can be repeated (e.g.hourly, daily, weekly, etc.) in order to monitor the progression of thefall risk of the subject over time. The result of the fall riskassessment can be provided or indicated to the subject or a healthcareprofessional.

The context information used to select a part of the first data set asdescribed above can also be used to provide information on the riskexposure of the subject (i.e. the likelihood of the subject falling),provide an indicator of the subject's mobility, and/or modify theinterpretation of the subject's movement in relation to fall risk whendetermining the fall risk.

For example, in the case of a subject that is a frail elderly personrecently discharged from hospital, it is useful to know whether thesubject was using a walking aid since this affects the subject's gaitand balance. This context information can improve the fall riskassessment by providing information about the subject's: risk exposure(e.g. this subject is less likely to fall when walking if using thewalking aid compared to walking without using the walking aid); mobilityindicator (e.g. subjects who need or feel the need to use a walking aidhave a higher risk of falling compared to subjects not using a walkingaid at all, even if they have the same risk exposure); and change inmovement pattern caused by the context (e.g. the use of a walking aidchanges movement patterns, which can be accounted for in the fall riskassessment of the walking movement).

Thus, as well as using the context information regarding the walking aidto select only part of the first data set where the subject is walkingand using a walking aid (or alternatively is walking but not using awalking aid), the context information can be used by the fall riskassessment algorithm to interpret the quality of the walking movementappropriately.

As noted above, the sensor(s) 10 that obtain the context information canbe sensors in a ‘smart home’ system (e.g. a system where many devicesand objects in a home environment are interconnected, and/or where thelocation and/or activities of a subject can be monitored). Such a systemcan use sensors, such as passive infrared (PIR) sensors and/oropen-close (OC) sensors on doors, the refrigerator, etc. to monitor thelocation and activities of the subject. Alternatively or in additionpressure mats (e.g. floor mats that include pressure sensors) can beplaced on the floor to detect the presence of a subject in that room orlocation, and/or sensors on appliances or devices (e.g. on a kettle) canmeasure the activation and use of such an appliance or device.

Depending on the specific implementation, the sensors in the ‘smarthome’ system may be able to provide context information on the‘activities of daily living’ (ADL) of the subject. For example thesensors can provide context information that allows the ADL to beinferred, such as the subject sleeping, the subject being present in aparticular location/room, being out of the home environment, watchingTV, eating and drinking, physical movements, sleep efficiency, using thetoilet, bathing, detecting visitors. These ADLs can be inferred from apattern of events at various sensors, such as various PIR and OC sensorsthat are distributed around the home. For example, the subject leavingthe house can be detected by a combination of OC sensors mounted at thefront (exit) door, and the presence of the subject before the OC eventand no presence after that OC event, as measured by a PIR sensor in thehall way near to the door. Sleeping can be detected from a pressure matsthat is located below the mattress of the subject's bed. Alternatively,measurements from a PIR sensor in the bedroom can be used to infer thatthe subject is sleeping. Eating and drinking, or meal preparation, canbe inferred from detecting activity in the kitchen, measured by a PIRsensor in the kitchen, and/or with the usage of typical appliances likea refrigerator and a microwave, measured by OC sensors (or movementsensors) mounted to those appliances. Using the toilet can be inferredfrom measurements by a PIR sensor in the toilet. Similarly the subjectbathing can be inferred from measurements by a PIR sensor in thebathroom.

Although the context sensor(s) described above are part of a ‘smarthome’ system, it will be appreciated that these sensors can be providedjust for the purposes of measuring context information for use in themethod of FIG. 2.

Some specific examples of context information and/or devices or objectsfrom which context information can be obtained that can be used toselect the part of the first data set are set out below. It will beappreciated that any two or more of these examples can be used togetheror be part of the same apparatus.

Weighing scales—as well as providing a weight measurement of thesubject, it can be useful to know when the subject was standing on theweighing scales since they will be aiming to stand upright and steadywhile the weight measurement is taken. The amount of body sway while thesubject is standing on the weighing scales can be used to assess thefall risk (with, generally, higher body sway indicating a higher fallrisk), and thus the weighing scales can comprise a context sensor 10that outputs a second data set that indicates when the subject isstanding on the weighing scales. In this case the context sensor 10 canbe, for example, a pressure sensor located in, on or under the weighingscales, or a power sensor (in the case of electronic weighing scales).Alternatively where the weighing scales are a ‘connected’ device (i.e.where measurements or sensor data can be communicated from the weighingscales to another device) the context sensor 10 can be the weight sensorin the weighing scales. The selected part of the first data set will bethe portion(s) of the first data set that correspond in time to when thesecond data set indicates that the subject is standing on the weighingscales.

Light sensor—it is useful to know the lighting conditions in theenvironment in which the subject is in, and to evaluate movements underthe same lighting conditions when assessing fall risk. In low light thesubject may be more unstable when walking than in bright lightconditions. Thus the context sensor 10 can be a light sensor thatmeasures the light level. The light sensor could be worn or carried bythe subject (in the same way as the movement sensor 4, or it could belocated in the environment of the subject (in which case multiple lightsensors may be provided in respective locations to provide light levelmeasurements wherever the subject is located). In the latter case, afurther sensor may be provided that can detect the presence of thesubject in the location in which the light sensor is located. In thisway, it is possible to determine where the subject is located andtherefore which light sensor measurement should be used for the seconddata set. The presence sensor can be a PIR sensor, for example. Theselected part of the first data set will be the portion(s) of the firstdata set where the light level is the same or similar (e.g. where eachmeasurement is within a particular range). Preferably the selected partcomprises the portion(s) of the first data set where the subject isperforming a particular type of movement (e.g. walking, standing, etc.)and where the light level is the same or similar.

Walking aid—a walking aid can be anything that helps a subject to walkor to walk more steadily than when unaided. A walking aid can be awalking stick or a walking frame, for example. For fall risk assessmentit is useful to know if the subject was using a walking aid, so that thefall risk assessment can be based on walking movements with a similarcontext (e.g. only walking is assessed where the subject is using awalking aid or only walking is assessed where the subject is not using awalking aid). In some embodiments, as described above, theinterpretation of the subject's movements by the fall risk assessmentalgorithm can be modified based on the indication of whether the subjectis/was using a walking aid. Thus, in this example the context sensor 10can be a movement sensor that is located on or in the walking aid andthat indicates when the walking aid is being moved. It will beappreciated in this case that the movement sensor on or in the walkingaid is a separate movement sensor to movement sensor 4 that measures themovements of the subject. Alternatively the sensor 10 can be a sensorthat detects contact or proximity between the subject and the walkingaid, with contact (in conjunction with walking or other movements by thesubject) being indicative of the walking aid being used. A suitablesensor 10 in this case can be a skin conductance sensor or a pressuresensor on a handle of the walking aid. For a fall risk assessment basedon the subject's walking (or other movements) when the subject is usingthe walking aid, the selected part of the first data set will be theportion(s) of the first data set where the subject is walking (orperforming other movements) and the second data set indicates that thewalking aid is also moving or being used. For a fall risk assessmentbased on the subject's walking (or other movements) when the subject isnot using the walking aid, the selected part of the first data set willbe the portion(s) of the first data set where the subject is walking (orperforming other movements) and the second data set indicates that thewalking aid is not being moved or used. It will be appreciated that ineither case the fall risk can be assessed based on an estimation of gaitstability (e.g. by determining Lyapunov exponents or footstep timevariance) or by estimating gait regularity from the movementmeasurements. It will also be appreciated that a fall risk can beassessed using these estimations for walking without any informationabout the use or non-use of a walking aid. In some embodiments, the fallrisk assessment may comprise determining a fall risk when the subject isnot using the walking aid, determining a fall risk when the subject isusing the walking aid, and comparing the two determined fall risks. Thiscomparison would show the level of dependence of the subject on thewalking aid, and/or the effectiveness of the use of the walking aid toreduce fall risk.

Bed—it is useful to know when the subject has got out of bed sincegetting up quickly after a long period in bed is associated with animmediate drop in blood pressure and thus dizziness (it will beappreciated that this is also applicable to getting up from a chair). Inaddition or alternatively it can be useful to know the length of timethat the subject has been in bed, since the amount and/or quality ofsleep that the subject has had can impact their fall risk (typicallymore tired means that they are at a higher risk of falling). Thus, thecontext sensor 10 can be used to determine or detect the presence and/ormovements of the subject in bed. In this case the context sensor 10 canbe a pressure sensor that is positioned on or in the bed (e.g. below amattress), or a sensor that can observe the movement of the subject intheir bedroom (e.g. a camera or imaging device, with the images beingprocessed to identify the posture and/or movements of the subject).Alternatively, a separate context sensor 10 may not be required as a‘getting-up-from-bed’ movement can be detected in the movementmeasurements from the movement sensor 4. The selected part of the firstdata set can be the portion(s) of the first data set where the subjectis out of bed, alternatively where the subject has just got out of bed,or alternatively at least a predetermined amount of time after thesubject has got out of bed. In some embodiments, the length of timesince the subject got out of bed can be used as an indicator of howtired the subject is, and in this case the selected part of the firstdata set can be movements where the subject has the same or a similarlevel of tiredness (e.g. the same or a similar length of time since thesubject got out of bed).

Chair—one way in which fall risk can be assessed is from the subject'sability to stand up from sitting on a chair (known as a sit to standtransfer (STS)). However chairs can be of different sizes and shapes,and include/not include arm rests, etc., so it is useful to identify thechair that the subject was sitting on. Thus, the context sensor 10 canbe a sensor that measures the presence of a subject in a particularchair, and the selected part of the first data set can be the portion(s)of the first data set corresponding to a sit to stand movement where thesubject was sitting on the same chair, or same type of chair. Thisallows the STS movements to be compared when assessing the fall risk.

Toilet—this is similar to the chair embodiment above in that a sit tostand movement from the toilet can be assessed, and thus it is useful toknow when a sit to stand movement from a toilet occurred. In this casethe context sensor 10 can be a sensor that detects the presence of thesubject in a bathroom and/or detects that the subject is sat on thetoilet.

Hallway/corridor—a subject will typically walk in a straight line down ahallway or corridor, and it can be useful to assess the fall risk of thesubject from this walking (specifically the gait stability and/or gaitregularity) since the walking ‘scenario’ is consistent (e.g. similarwalk length, similar location, restricted options for where to walk,similar walking intention (e.g. walking to the kitchen, etc.), etc.). Inthis case the context sensor 10 can be a sensor that detects thepresence of the subject in the hallway or corridor, and for example thesensor 10 can be a PIR sensor, pressure sensor (e.g. located in or onthe floor) or an imaging unit (e.g. camera). Thus the selected part ofthe first data set can be the portion(s) in which the subject is locatedin the hallway or corridor. Alternatively the selected part of the firstdata set can be the portion(s) in which the subject is determined to belocated in the hallway or corridor and is determined to be walking.Further useful context information can be provided by a light sensorthat measures the light level in the hallway or corridor, and thus theselected part of the first data set can further correspond to walking insimilar lighting conditions.

Medication dispenser—the fall risk of a subject may depend on anymedication that they are taking (or not, if they have missed a dose).For example blood pressure medication, anti-depressants and medicationfor Parkinson's disease have an influence on the ambulatory ability ofthe subject, and thus it is useful to know if the subject has recentlyor is consistently taking certain types of medication. Thus, where thesubject obtains the medication from a medication dispenser (e.g. adevice that stores their medication and dispenses the required dose at arequired time), the context sensor 10 can be a sensor that detects whena dose of medication has been removed from the medication dispenserand/or a sensor that detects the type of medication that has beendispensed. The selected part of the first data set can be the portion(s)of the movements of the subject where the subject has taken the sametype of medication and/or the portion(s) where the subject took themedication a certain time period previously (e.g. more than one hourago, etc.).

Ambient noise or sound level—the noise or sound level around the subjectcan provide an indication of how distracted the subject is (e.g. theyare talking/being talked to, traffic noise, tone of the voice of theperson, silence, etc.).

Other examples of context information include the location, e.g.measured using GPS, in which case the selected part may correspond tomovements in the same or similar location, or movements following thesame path, the weather conditions or temperature, in which case theselected part may correspond to those movements where the weather is thesame or the temperature is the same (or within a predetermined range),the time of day, in which case the selected part may correspond to thosemovements during the same or a similar time of day, e.g. in the morning,afternoon, etc.

There is therefore provided an improved method and apparatus fordetermining a fall risk.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, such illustration and descriptionare to be considered illustrative or exemplary and not restrictive; theinvention is not limited to the disclosed embodiments.

Variations to the disclosed embodiments can be understood and effectedby those skilled in the art in practicing the claimed invention, from astudy of the drawings, the disclosure, and the appended claims. In theclaims, the word “comprising” does not exclude other elements or steps,and the indefinite article “a” or “an” does not exclude a plurality. Asingle processor or other processing unit may fulfil the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Acomputer program may be stored/distributed on a suitable medium, such asan optical storage medium or a solid-state medium supplied together withor as part of other hardware, but may also be distributed in otherforms, such as via the Internet or other wired or wirelesstelecommunication systems. Any reference signs in the claims should notbe construed as limiting the scope.

1. A computer-implemented method of determining a fall risk of a subject, the method comprising: receiving a first data set containing measurements of movements of the subject over a period of time; receiving a second data set indicative of context information of the subject, wherein the second data set contains measurements that cover the same period of time as is covered by the first data set; selecting a part of the first data set based on the second data set; and determining a fall risk based on movement measurements in the selected part of the first data set.
 2. The computer-implemented method as claimed in claim 1, wherein the step of selecting comprises: using the second data set to select one or more portions of the first data set that have the same or similar context information.
 3. The computer-implemented method as claimed in claim 1, wherein the method further comprises: processing the first data set to identify occurrences of a particular type of movement performed by the subject; wherein the step of selecting comprises using the second data set to select one or more portions of the first data set that relate to occurrences of the particular type of movement and that have the same or similar context information.
 4. The computer-implemented method as claimed in claim 3, wherein the particular type of movement comprises any one or more of walking, jogging, running, getting up from a sitting position, exercising or standing still.
 5. The computer-implemented method as claimed in claim 1, wherein the context information comprises any one or more of the location of the subject, the light level, whether the subject is using a walking aid, whether or when the subject is in bed, whether or when the subject is standing still, whether the subject is using a specific chair, whether or when the subject is using the toilet, whether or when the subject has taken medication, the temperature, the weather conditions, or the time of day.
 6. The computer-implemented method as claimed in claim 1, the method further comprising the step of: measuring the movement of the subject to provide the first data set.
 7. The computer-implemented method as claimed in claim 1, the method further comprising the step of: measuring context information of the subject to provide the second data set.
 8. A computer program product comprising a computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform the method of claim
 1. 9. An apparatus for determining a fall risk of a subject, the apparatus comprising: a processing unit configured to: receive a first data set containing measurements of movements of the subject over a period of time; receive a second data set indicative of context information of the subject, wherein the second data set contains measurements that cover the same period of time as is covered by the first data set; select a part of the first data set based on the second data set; and determine a fall risk based on movement measurements in the selected part of the first data set.
 10. The apparatus as claimed in claim 9, wherein the processing unit is configured to select a part of the first data set by using the second data set to select one or more portions of the first data set that have the same or similar context information.
 11. The apparatus as claimed in claim 9, wherein the processing unit is configured to process the first data set to identify occurrences of a particular type of movement performed by the subject; and to select a part of the first data set by using the second data set to select one or more portions of the first data set that relate to occurrences of the particular type of movement and that have the same or similar context information.
 12. The apparatus as claimed in claim 11, wherein the particular type of movement comprises any one or more of walking, jogging, running, getting up from a sitting position, exercising or standing still.
 13. The apparatus as claimed in claim 9, wherein the context information comprises any one or more of the location of the subject, the light level, whether the subject is using a walking aid, whether or when the subject is in bed, whether or when the subject is standing still, whether the subject is using a specific chair, whether or when the subject is using the toilet, whether or when the subject has taken medication, the temperature, the weather conditions, or the time of day.
 14. The apparatus as claimed in claim 9, wherein the apparatus further comprises a movement sensor for measuring the movement of the subject to provide the first data set.
 15. The apparatus as claimed in claim 9, wherein the apparatus further comprises a context sensor for measuring context information of the subject to provide the second data set. 